This paper proposes a method for smoothing the Hidden Markov Models (HMM) with the VQ done by means of the Self Organising Feature Maps (SOFM). The use SOFM gives rise to a special property of the probability of emission matrix of the HMM. This property is that when ordering the probability of emission matrix following the order of the SOFM; neighbouring symbols will have similar probabilities. In order to smooth the HMM we propose to filter the probability of emission matrix by a filter that makes use of this property. We also compare this method with another method for smoothing the HMM; the coocurrence method. The recognition rate improvement achieved by the method that we propose is better than the recognition rate obtained by means of the coocurrence method.
Cite as: Monte, E., Marino, J.B., LLeida, E. (1992) Smoothing hidden Markov models ay means of a self organizing feature map. Proc. 2nd International Conference on Spoken Language Processing (ICSLP 1992), 535-538, doi: 10.21437/ICSLP.1992-119
@inproceedings{monte92_icslp,
author={E. Monte and José B. Marino and Eduardo LLeida},
title={{Smoothing hidden Markov models ay means of a self organizing feature map}},
year=1992,
booktitle={Proc. 2nd International Conference on Spoken Language Processing (ICSLP 1992)},
pages={535--538},
doi={10.21437/ICSLP.1992-119}
}